1. Field of the Invention
The present invention relates to the petroleum industry, and more particularly to the development of underground reservoirs such as petroleum reservoirs or gas storage sites. In particular, the invention allows modification of a representation of the reservoir, referred to as reservoir model, in order to make it coherent with various data collected in the field.
2. Description of the Prior Art
Optimization and development of petroleum reservoirs are based on the most accurate possible description of the structure, the petrophysical properties, the fluid properties, etc., of the reservoir. A tool accounting for these aspects in an approximate way is a reservoir model which is a model of the subsoil, representative of both its structure and its behavior. Generally, this model is represented in a computer and is referred to as a numerical model. A reservoir model comprises a grid, generally three-dimensional, associated with one or more petrophysical property maps (porosity, permeability, saturation, etc.). The association assigns values of these petrophysical properties to each cell of the grid.
These models which are well known and widely used in the petroleum industry, allow determination of many technical parameters relative to the study or the development of a reservoir such as a hydrocarbon reservoir. In fact, since the reservoir model is representative of the structure of the reservoir and of the behavior thereof, engineers use the model to determine which zones are the most likely to contain hydrocarbons, the zones in which it can be interesting/necessary to drill an injection or a production well in order to enhance hydrocarbon recovery, the type of tools to use, the properties of the fluids used and recovered, etc. These interpretations of reservoir models in terms of “technical development parameters” are well known. Similarly, modelling CO2 storage sites allows monitoring these sites, to detect abnormal behaviours and to predict the displacement of the injected CO2.
The purpose of a reservoir model thus is to best account for all the available information on a reservoir. A reservoir model is representative when a reservoir simulation provides historical data estimations that are very close to the observed data. What is referred to as historical data are the production data obtained from measurements in wells in response to the reservoir production (oil production, water production of one or more wells, gas/oil ratio (GOR), production water proportion (water cut)), and/or repetitive seismic data (4D seismic impedances in one or more regions, etc.). A reservoir simulation is a technique allowing simulation of fluid flows within a reservoir by software referred to as the flow simulator, and the reservoir model.
Integration of all the available data is therefore essential. These data generally comprise:                measurements at certain points of the geological formation, in wells for example. These data are referred to as static because they are invariable in time (on the scale of the reservoir production times);        “historical data”, comprising production data, for example the fluid flow rates measured in wells, tracer concentrations and data obtained from repetitive seismic acquisition campaigns at successive times. These data are referred to as dynamic because they evolve during the development and they are indirectly linked with the properties assigned to the cells of the reservoir model.        
Techniques for integration of dynamic data (production and/or 4 D seismic) in a reservoir model are known and are referred to as “history matching ”techniques.
History matching modifies the parameters of a reservoir model, such as permeabilities, porosities or well skins (representing damages around the well), fault connections, etc., in order to minimize the differences between the simulated and measured historical data. The parameters can be linked with geographic regions, such as permeabilities or porosities around one or more wells. The difference between real data and simulated data forms a functional referred to as objective function. The history matching problem is solved by minimizing this functional. The objective function is usually calculated as the sum of the square of the errors between simulated data and measured data. This formulation has proved efficient for the production data insofar as it characterizes correctly the error observed between the simulated and measured data, and it is significantly reduces during the optimization process. For the seismic data, this formulation is not suitable because it is not representative of the observed difference between two seismic attribute images. The optimization process can therefore not reduce it significantly.